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solid booster formulations with PFAS-free binders, in line with the sustainability goals of the project. You will explore compositions and pellet morphologies to optimize mass transport and reactivity
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Our research group focuses on the development of AI algorithms for industrial applications. The main scope of our activities is the optimization and automation of workflows and production systems
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performance of large-scale experiments. LLM post-training and Reinforcement Learning Support SFT, preference optimization, and reinforcement learning workflows. Build and run RL environments for tasks with
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the Rheintal project Integrate CCUS and industrial processes into the energy system models Assess the energy system towards different metrics and optimization objectives such as cost and CO2 emissions Coordinate
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optimize, specialize, and deploy models — fine-tuning, distillation, quantization, and efficient inference. Contribute to the evaluation and benchmarking of AI systems on scientific tasks, including open and
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publication record in machine learning, artificial intelligence, optimization, or related areas. Excellent communication skills and fluency in English (spoken and written) are required. We also seek to increase
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data manipulation techniques that exploit optimally the underlying hardware and I/O devices, and we enable new discoveries in scientific domains through automating physical database design and
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application. Develop customized ILC and parameter optimization algorithms for industrial applications, going from theoretical analysis to implementation and testing on real systems Develop adaptive control and
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may include AI/ML (incl. LLMs), XR, robotics, simulation, optimization, or others that fit your research question. We welcome foundational AI and XR proposals (e.g., representations, verification
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, optimization, and modernization of infrastructure and workflows Support the reliability, scalability, and security of the platform services Profile Bachelor’s or Master’s degree in Computer Science